Parameterization of Extended Force Field using Graph Neural Nets

This presentation is a part of the Open Force Field Virtual Meeting 2020.

Abstract: By using graph neural networks capable of automatically perceiving chemical environments and directly producing parameters for a fast classical potential, we show it is possible to flexibly assign parameters to near-arbitrary biomolecular systems while achieving near-quantum chemical accuracy with molecular mechanics speed.